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The reliability of deteriorating structures at time t is quantified by the probability that failure occurs within the period leading up to time t. This probability is often referred to as cumulative failure probability and is equal to the cumulative distribution function of the time to failure. In structural reliability, an estimate of the cumulative failure probability is obtained based on probabilistic engineering models of the deterioration processes and structural performance. Information on the condition and the loading contained in inspection and monitoring data can be included in the probability estimate through Bayesian updating. Conditioning the probability of failure on the inspection or monitoring outcomes available at time t (e.g. detections or no detection of damages) can lead to a reduction in that probability.
Such a drop in the cumulative failure probability might seem counterintuitive since the cumulative failure probability is a non-decreasing function of time. In this paper, we illustrate—with the help of a numerical example—that such a drop is possible because the cumulative probability before and after the updating is not based on the same information, hence not on the same probabilistic model.
We present a concept for assessing the system reliability of monitored jacket support struc-tures of offshore wind turbines subjected to fatigue. The concept assumes that a structural health monitoring system periodically records accelerations of the jacket structure caused by ambient excitations. The recorded data is processed by a stochastic subspace-based damage detection algorithm to provide information on the global damage state of the jacket structure. This information is generally uncertain. To determine the effect of such uncertain global monitoring information on the structural reliability, the deterioration state of the jacket structure is described by a probabilistic fatigue deterioration model of all structural elements, which considers stochastic dependence among element fatigue behavior. The system deterioration model is coupled with a probabilistic structural model to compute the system failure probability. Global damage detection information is included in the reliability assessment through Bayesian updating of the system failure probability.
Das Ermüdungsverhalten einzelner Schweißverbindungen in Offshore-Strukturen ist stark korreliert. Diese Korrelation entsteht zum einen durch die Verwendung gleicher Prozesse und Materialien bei der Herstellung der Verbindungen, zum anderen sind die ermüdungswirksamen Lasten an verschiedene Hospots im Tragwerk ebenfalls von einander abhängig. Hieraus ergeben sich Systemeffekte, die es ermöglichen, durch stichprobenartige Inspektion des Zustandes einzelner Hotspots auf den Zustand der verbleibenden Hotspots im Tragwerk zu schließen. Auf dieser Grundlage können Inspektions- und Reparaturstrategien für ermüdungsbeanspruchte Offshore-Strukturen mit Hilfe von riskobasierten Methoden optimiert werden. In diesem Vortrag wird weiterführend ein Konzept zur Einbindung von globalen Monitoringergebnissen in die risikobasierte Inspektionsplanung vorgestellt.